os research paper 2013
Journal of Computing and Information Technology - CIT 10, 2002, 2, 69–84
69
A Procedure of Conversion of Relational into Multidimensional
Database Schema
Mladen Varga
Faculty of Economics, University of Zagreb, Croatia
It is universally recognized that operational information systems lean on the relational model and data warehouses on the multidimensional model. The phrase On-Line
Analytical Processing (OLAP) means summarizing, consolidating, viewing, and synthesizing data according to multiple dimensions. The process of modeling data warehouse may start from operational system’s database.
It may be helpful to convert a relational database schema into a multidimensional database schema in order to discover dimensions that are hidden in a relational database.
However, only a few efforts have been done investigating the conversion of relational into multidimensional database schema. This paper proposes the general procedure of such a conversion. The procedure can be partly automated because some decisions of attribute type must be made during conversion.
Keywords: relational database, relational schema, multidimensional database, multidimensional schema, conversion.
1. Introduction
Operational information systems that lean on the relational model may be a starting point for the process of modeling data warehouse. Most data warehouses rely on the multidimensional model and it may be helpful to convert a relational database schema into a multidimensional database schema.
In this section, some general facts of relational and dimensional model are mentioned. In Section 2 the procedure of conversion is presented and in Section 3 a small example of the conversion is shown.
1.1. Relational Model
A relational database consists of a set of relations. A relational schema which is used to describe a relation r, denoted by R(A1 A2 : : : An ) is made up of a relation named R and a list of attributes A1 , A2 ,: : : , An . Each attribute
69
A Procedure of Conversion of Relational into Multidimensional
Database Schema
Mladen Varga
Faculty of Economics, University of Zagreb, Croatia
It is universally recognized that operational information systems lean on the relational model and data warehouses on the multidimensional model. The phrase On-Line
Analytical Processing (OLAP) means summarizing, consolidating, viewing, and synthesizing data according to multiple dimensions. The process of modeling data warehouse may start from operational system’s database.
It may be helpful to convert a relational database schema into a multidimensional database schema in order to discover dimensions that are hidden in a relational database.
However, only a few efforts have been done investigating the conversion of relational into multidimensional database schema. This paper proposes the general procedure of such a conversion. The procedure can be partly automated because some decisions of attribute type must be made during conversion.
Keywords: relational database, relational schema, multidimensional database, multidimensional schema, conversion.
1. Introduction
Operational information systems that lean on the relational model may be a starting point for the process of modeling data warehouse. Most data warehouses rely on the multidimensional model and it may be helpful to convert a relational database schema into a multidimensional database schema.
In this section, some general facts of relational and dimensional model are mentioned. In Section 2 the procedure of conversion is presented and in Section 3 a small example of the conversion is shown.
1.1. Relational Model
A relational database consists of a set of relations. A relational schema which is used to describe a relation r, denoted by R(A1 A2 : : : An ) is made up of a relation named R and a list of attributes A1 , A2 ,: : : , An . Each attribute
References: ACM Workshop on Data Warehousing and OLAP, (1998), Washington. of the Hawaii Int. Conf. on System Science, (1998a), Kona. Conference, (1996), Mumbai (Bombay). 4 ] R. KIMBALL, The Data Warehouse Toolkit. John Wiley, (1996), New York. 10th Int. Conf. on Scient. and Statistical Database Management, (1998), pp Systems, (1997), pp. 185-196. Received: November, 1999 Revised: March, 2002 Accepted: May, 2002 Contact address: